Generally speaking, the “best” algorithm is created iteratively through qualitative
means: testing, algorithmic study, etc. The number of iterations can be reduced in
two ways. First, through more accurate problem modeling. Second, through quantitative
measurements of principles in such a way that performance on variants of a given
evaluated problem can be accurately predicted for a given iteration. However, to the
best of our knowledge, a design methodology encompassing both of these does not
exist, and is a major reason why there has not been more extensive application of
swarm intelligence to real-world problems [1]}. Previous
work ([2]}, [3]}, [4]}) in swarm engineering
directly investigating algorithms for a target application only
implicitly considers the swarm properties of scalability, emergent self-organization,
robustness, and flexibility through raw performance comparisons (with the exception
of [4]}, which presented a methodology partially capable of (1)
and (2)).
